library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
Data from the speech features
TADPOLE_D1_D2 <- read.csv("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2.csv")
TADPOLE_D1_D2_Dict <- read.csv("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2_Dict.csv")
TADPOLE_D1_D2_Dict_LR <- as.data.frame(read_excel("~/GitHub/BSWiMS/Data/TADPOLE/TADPOLE_D1_D2_Dict_LR.xlsx",sheet = "LeftRightFeatures"))
rownames(TADPOLE_D1_D2_Dict) <- TADPOLE_D1_D2_Dict$FLDNAME
# mm3 to mm
isVolume <- c("Ventricles","Hippocampus","WholeBrain","Entorhinal","Fusiform","MidTemp","ICV",
TADPOLE_D1_D2_Dict$FLDNAME[str_detect(TADPOLE_D1_D2_Dict$TEXT,"Volume")]
)
#TADPOLE_D1_D2[,isVolume] <- apply(TADPOLE_D1_D2[,isVolume],2,'^',(1/3))
TADPOLE_D1_D2[,isVolume] <- TADPOLE_D1_D2[,isVolume]^(1/3)
# mm2 to mm
isArea <- TADPOLE_D1_D2_Dict$FLDNAME[str_detect(TADPOLE_D1_D2_Dict$TEXT,"Area")]
TADPOLE_D1_D2[,isArea] <- sqrt(TADPOLE_D1_D2[,isArea])
# Get only cross sectional measurements
FreeSurfersetCross <- str_detect(colnames(TADPOLE_D1_D2),"UCSFFSX")
# The subset of baseline measurements
baselineTadpole <- subset(TADPOLE_D1_D2,VISCODE=="bl")
table(baselineTadpole$DX)
Dementia Dementia to MCI MCI MCI to Dementia
7 336 1 864 5
MCI to NL NL NL to MCI
2 521 1
table(baselineTadpole$DX_bl)
AD CN EMCI LMCI SMC 342 417 310 562 106
rownames(baselineTadpole) <- baselineTadpole$PTID
validBaselineTadpole <- cbind(DX=baselineTadpole$DX_bl,
AGE=baselineTadpole$AGE,
Gender=1*(baselineTadpole$PTGENDER=="Female"),
ADAS11=baselineTadpole$ADAS11,
ADAS13=baselineTadpole$ADAS13,
MMSE=baselineTadpole$MMSE,
RAVLT_immediate=baselineTadpole$RAVLT_immediate,
RAVLT_learning=baselineTadpole$RAVLT_learning,
RAVLT_forgetting=baselineTadpole$RAVLT_forgetting,
RAVLT_perc_forgetting=baselineTadpole$RAVLT_perc_forgetting,
FAQ=baselineTadpole$FAQ,
Ventricles=baselineTadpole$Ventricles,
Hippocampus=baselineTadpole$Hippocampus,
WholeBrain=baselineTadpole$WholeBrain,
Entorhinal=baselineTadpole$Entorhinal,
Fusiform=baselineTadpole$Fusiform,
MidTemp=baselineTadpole$MidTemp,
ICV=baselineTadpole$ICV,
baselineTadpole[,FreeSurfersetCross])
LeftFields <- TADPOLE_D1_D2_Dict_LR$LFN
names(LeftFields) <- LeftFields
LeftFields <- LeftFields[LeftFields %in% colnames(validBaselineTadpole)]
RightFields <- TADPOLE_D1_D2_Dict_LR$RFN
names(RightFields) <- RightFields
RightFields <- RightFields[RightFields %in% colnames(validBaselineTadpole)]
## Normalize to ICV
validBaselineTadpole$Ventricles=validBaselineTadpole$Ventricles/validBaselineTadpole$ICV
validBaselineTadpole$Hippocampus=validBaselineTadpole$Hippocampus/validBaselineTadpole$ICV
validBaselineTadpole$WholeBrain=validBaselineTadpole$WholeBrain/validBaselineTadpole$ICV
validBaselineTadpole$Entorhinal=validBaselineTadpole$Entorhinal/validBaselineTadpole$ICV
validBaselineTadpole$Fusiform=validBaselineTadpole$Fusiform/validBaselineTadpole$ICV
validBaselineTadpole$MidTemp=validBaselineTadpole$MidTemp/validBaselineTadpole$ICV
leftData <- validBaselineTadpole[,LeftFields]/validBaselineTadpole$ICV
RightData <- validBaselineTadpole[,RightFields]/validBaselineTadpole$ICV
## get mean and relative difference
meanLeftRight <- (leftData + RightData)/2
difLeftRight <- abs(leftData - RightData)
reldifLeftRight <- difLeftRight/meanLeftRight
colnames(meanLeftRight) <- paste("M",colnames(meanLeftRight),sep="_")
colnames(difLeftRight) <- paste("D",colnames(difLeftRight),sep="_")
colnames(reldifLeftRight) <- paste("RD",colnames(reldifLeftRight),sep="_")
validBaselineTadpole <- validBaselineTadpole[,!(colnames(validBaselineTadpole) %in%
c(LeftFields,RightFields))]
validBaselineTadpole <- cbind(validBaselineTadpole,meanLeftRight,difLeftRight,reldifLeftRight)
#validBaselineTadpole <- cbind(validBaselineTadpole,meanLeftRight,difLeftRight)
#validBaselineTadpole <- cbind(validBaselineTadpole,leftData,RightData)
## Remove columns with too many NA more than %15 of NA
nacount <- apply(is.na(validBaselineTadpole),2,sum)/nrow(validBaselineTadpole) < 0.15
diagnose <- validBaselineTadpole$DX
pander::pander(table(diagnose))
| AD | CN | EMCI | LMCI | SMC |
|---|---|---|---|---|
| 342 | 417 | 310 | 562 | 106 |
validBaselineTadpole <- validBaselineTadpole[,nacount]
## Remove character columns
ischar <- sapply(validBaselineTadpole,class) == "character"
validBaselineTadpole <- validBaselineTadpole[,!ischar]
## Place back diagnose
validBaselineTadpole$DX <- diagnose
validBaselineTadpole <- validBaselineTadpole[complete.cases(validBaselineTadpole),]
ischar <- sapply(validBaselineTadpole,class) == "character"
validBaselineTadpole[,!ischar] <- sapply(validBaselineTadpole[,!ischar],as.numeric)
colnames(validBaselineTadpole) <- str_remove_all(colnames(validBaselineTadpole),"_UCSFFSX_11_02_15_UCSFFSX51_08_01_16")
colnames(validBaselineTadpole) <- str_replace_all(colnames(validBaselineTadpole)," ","_")
validBaselineTadpole$LONISID <- NULL
validBaselineTadpole$IMAGEUID <- NULL
validBaselineTadpole$LONIUID <- NULL
diagnose <- as.character(validBaselineTadpole$DX)
validBaselineTadpole$DX <- diagnose
pander::pander(table(validBaselineTadpole$DX))
| AD | CN | EMCI | LMCI | SMC |
|---|---|---|---|---|
| 245 | 359 | 272 | 444 | 93 |
validBaselineTadpole[validBaselineTadpole$DX %in% c("EMCI","LMCI"),"DX"] <- "MCI"
validBaselineTadpole[validBaselineTadpole$DX %in% c("CN","SMC"),"DX"] <- "NL"
pander::pander(table(validBaselineTadpole$DX))
| AD | MCI | NL |
|---|---|---|
| 245 | 716 | 452 |
subjectsID <- rownames(validBaselineTadpole)
visitsID <- unique(TADPOLE_D1_D2$VISCODE)
baseDx <- TADPOLE_D1_D2[TADPOLE_D1_D2$VISCODE=="bl",c("PTID","DX","EXAMDATE")]
rownames(baseDx) <- baseDx$PTID
baseDx <- baseDx[subjectsID,]
lastDx <- baseDx
toDementia <- baseDx
table(lastDx$DX)
Dementia Dementia to MCI MCI MCI to Dementia MCI to NL
244 1 711 2 2
NL NL to MCI
452 1
hasDementia <- lastDx$PTID[str_detect(lastDx$DX,"Dementia")]
for (vid in visitsID)
{
DxValue <- TADPOLE_D1_D2[TADPOLE_D1_D2$VISCODE==vid,c("PTID","DX","EXAMDATE")]
rownames(DxValue) <- DxValue$PTID
DxValue <- DxValue[DxValue$PTID %in% subjectsID,]
noDX <- DxValue$PTID[nchar(DxValue$DX) < 1]
print(length(noDX))
DxValue[noDX,] <- lastDx[noDX,]
inLast <- lastDx$PTID[lastDx$PTID %in% DxValue$PTID]
print(length(inLast))
lastDx[inLast,] <- DxValue[inLast,]
noDementia <- !(toDementia$PTID %in% hasDementia)
toDementia[noDementia,] <- lastDx[noDementia,]
hasDementia <- unique(c(hasDementia,lastDx$PTID[str_detect(lastDx$DX,"Dementia")]))
}
[1] 0 [1] 1413 [1] 2 [1] 1326 [1] 6 [1] 1218 [1] 23 [1] 1095 [1] 805 [1] 1058 [1] 29 [1] 710 [1] 20 [1] 212 [1] 14 [1] 167 [1] 32 [1] 553 [1] 25 [1] 298 [1] 18 [1] 130 [1] 667 [1] 667 [1] 112 [1] 112 [1] 176 [1] 176 [1] 177 [1] 177 [1] 625 [1] 625 [1] 251 [1] 251 [1] 159 [1] 159 [1] 7 [1] 7 [1] 17 [1] 99 [1] 9 [1] 63 [1] 1 [1] 1
table(lastDx$DX)
Dementia Dementia to MCI MCI MCI to Dementia MCI to NL
428 2 463 80 7
NL NL to Dementia NL to MCI
406 1 26
baseMCI <-baseDx$PTID[baseDx$DX == "MCI"]
lastDementia <- lastDx$PTID[str_detect(lastDx$DX,"Dementia")]
lastDementia2 <- toDementia$PTID[str_detect(toDementia$DX,"Dementia")]
lastNL <- lastDx$PTID[str_detect(lastDx$DX,"NL")]
MCIatBaseline <- baseDx[baseMCI,]
MCIatEvent <- toDementia[baseMCI,]
MCIatLast <- lastDx[baseMCI,]
MCIconverters <- MCIatBaseline[baseMCI %in% lastDementia,]
MCI_No_converters <- MCIatBaseline[!(baseMCI %in% MCIconverters$PTID),]
MCIconverters$TimeToEvent <- (as.Date(toDementia[MCIconverters$PTID,"EXAMDATE"])
- as.Date(MCIconverters$EXAMDATE))
sum(MCIconverters$TimeToEvent ==0)
[1] 0
MCIconverters$AtEventDX <- MCIatEvent[MCIconverters$PTID,"DX"]
MCIconverters$LastDX <- MCIatLast[MCIconverters$PTID,"DX"]
MCI_No_converters$TimeToEvent <- (as.Date(lastDx[MCI_No_converters$PTID,"EXAMDATE"])
- as.Date(MCI_No_converters$EXAMDATE))
MCI_No_converters$LastDX <- MCIatLast[MCI_No_converters$PTID,"DX"]
MCI_No_converters <- subset(MCI_No_converters,TimeToEvent > 0)
MCIPrognosisIDs <- c(MCIconverters$PTID,MCI_No_converters$PTID)
TADPOLECrossMRI <- validBaselineTadpole[MCIPrognosisIDs,]
table(TADPOLECrossMRI$DX)
MCI 680
TADPOLECrossMRI$DX <- NULL
TADPOLECrossMRI$status <- 1*(rownames(TADPOLECrossMRI) %in% MCIconverters$PTID)
table(TADPOLECrossMRI$status)
0 1 436 244
studyName <- "TADPOLE"
dataframe <- TADPOLECrossMRI
outcome <- "status"
TopVariables <- 10
thro <- 0.60
cexheat = 0.15
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 680 | 477 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 436 | 244 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) >= 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.9996707
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> ICV D_ST17SV RD_ST56CV D_ST65SV D_ST61SV D_ST36CV
#> AGE ADAS11 ADAS13 MMSE RAVLT_immediate
#> 0.02521008 0.35924370 0.36134454 0.01890756 0.11344538
#> RAVLT_learning
#> 0.07773109
#>
#> Included: 476 , Uni p: 0.0003151261 , Base Size: 56 , Rcrit: 0.1307612
#>
#>
1 <R=1.000,thr=0.950>, Top: 153< 1 >.[Fa= 153 ]( 153 , 153 , 0 ),<|><>Tot Used: 306 , Added: 153 , Zero Std: 0 , Max Cor: 0.909
#>
2 <R=0.909,thr=0.900>, Top: 3< 1 >[Fa= 156 ]( 3 , 3 , 153 ),<|><>Tot Used: 312 , Added: 3 , Zero Std: 0 , Max Cor: 0.884
#>
3 <R=0.884,thr=0.800>, Top: 41< 1 >[Fa= 188 ]( 39 , 49 , 156 ),<|><>Tot Used: 359 , Added: 49 , Zero Std: 0 , Max Cor: 0.923
#>
4 <R=0.923,thr=0.900>, Top: 1< 1 >[Fa= 189 ]( 1 , 1 , 188 ),<|><>Tot Used: 360 , Added: 1 , Zero Std: 0 , Max Cor: 0.841
#>
5 <R=0.841,thr=0.800>, Top: 1< 1 >[Fa= 190 ]( 1 , 1 , 189 ),<|><>Tot Used: 362 , Added: 1 , Zero Std: 0 , Max Cor: 0.800
#>
6 <R=0.800,thr=0.700>, Top: 114< 1 >.[Fa= 275 ]( 112 , 120 , 190 ),<|><>Tot Used: 451 , Added: 120 , Zero Std: 0 , Max Cor: 0.748
#>
7 <R=0.748,thr=0.700>, Top: 1< 1 >[Fa= 276 ]( 1 , 1 , 275 ),<|><>Tot Used: 453 , Added: 1 , Zero Std: 0 , Max Cor: 0.696
#>
8 <R=0.696,thr=0.600>, Top: 44< 1 >[Fa= 292 ]( 43 , 59 , 276 ),<|><>Tot Used: 468 , Added: 59 , Zero Std: 0 , Max Cor: 0.680
#>
9 <R=0.680,thr=0.600>, Top: 3< 1 >[Fa= 294 ]( 3 , 3 , 292 ),<|><>Tot Used: 468 , Added: 3 , Zero Std: 0 , Max Cor: 0.746
#>
10 <R=0.746,thr=0.700>, Top: 1< 1 >[Fa= 295 ]( 1 , 1 , 294 ),<|><>Tot Used: 468 , Added: 1 , Zero Std: 0 , Max Cor: 0.599
#>
11 <R=0.599,thr=0.600>
#>
[ 11 ], 0.5988799 Decor Dimension: 468 Nused: 468 . Cor to Base: 305 , ABase: 476 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
1378
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
1286
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
0.827
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
0.664
varratio <- attr(DEdataframe,"VarRatio")
pander::pander(tail(varratio))
| La_RD_ST61SV | La_RD_ST65SV | La_RD_ST17SV | La_RD_ST52CV | La_RD_ST129CV | La_ST10CV |
|---|---|---|---|---|---|
| 0.00369 | 0.00358 | 0.00274 | 0.00272 | 0.00226 | 0.000658 |
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPLTM <- attr(DEdataframe,"UPLTM")
gplots::heatmap.2(1.0*(abs(UPLTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
Displaying the features associations
par(op)
#if ((ncol(dataframe) < 1000) && (ncol(dataframe) > 10))
#{
# DEdataframeB <- ILAA(dataframe,verbose=TRUE,thr=thro,bootstrap=30)
transform <- attr(DEdataframe,"UPLTM") != 0
tnames <- colnames(transform)
colnames(transform) <- str_remove_all(colnames(transform),"La_")
transform <- abs(transform*cor(dataframe[,rownames(transform)])) # The weights are proportional to the observed correlation
VertexSize <- attr(DEdataframe,"fscore") # The size depends on the variable independence relevance (fscore)
names(VertexSize) <- str_remove_all(names(VertexSize),"La_")
VertexSize <- 10*(VertexSize-min(VertexSize))/(max(VertexSize)-min(VertexSize)) # Normalization
VertexSize <- VertexSize[rownames(transform)]
rsum <- apply(1*(transform !=0),1,sum) + 0.01*VertexSize + 0.001*varratio[tnames]
csum <- apply(1*(transform !=0),2,sum) + 0.01*VertexSize + 0.001*varratio[tnames]
ntop <- min(10,length(rsum))
topfeatures <- unique(c(names(rsum[order(-rsum)])[1:ntop],names(csum[order(-csum)])[1:ntop]))
rtrans <- transform[topfeatures,]
csum <- (apply(1*(rtrans !=0),2,sum) > 1)
rtrans <- rtrans[,csum]
topfeatures <- unique(c(topfeatures,colnames(rtrans)))
print(ncol(transform))
#> [1] 468
transform <- transform[topfeatures,topfeatures]
print(ncol(transform))
#> [1] 20
if (ncol(transform)>100)
{
csum <- (apply(1*(transform !=0),2,sum) > 1) & (apply(1*(transform !=0),1,sum) > 1)
transform <- transform[csum,csum]
print(ncol(transform))
}
if (ncol(transform) < 150)
{
gplots::heatmap.2(transform,
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Red Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
VertexSize <- VertexSize[colnames(transform)]
gr <- graph_from_adjacency_matrix(transform,mode = "directed",diag = FALSE,weighted=TRUE)
gr$layout <- layout_with_fr
fc <- cluster_optimal(gr)
plot(fc, gr,
edge.width = 2*E(gr)$weight,
vertex.size=VertexSize,
edge.arrow.size=0.5,
edge.arrow.width=0.5,
vertex.label.cex=(0.15+0.05*VertexSize),
vertex.label.dist=0.5 + 0.05*VertexSize,
main="Top Feature Association")
}
par(op)
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after ILAA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.5988799
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
topvars <- univariate_BinEnsemble(dataframe,outcome)
lso <- LASSO_MIN(formula(paste(outcome,"~.")),dataframe,family="binomial")
topvars <- unique(c(names(topvars),lso$selectedfeatures))
pander::pander(head(topvars))
ADAS11, ADAS13, RAVLT_immediate, FAQ, Hippocampus and WholeBrain
# names(topvars)
#if (nrow(dataframe) < 1000)
#{
datasetframe.umap = umap(scale(dataframe[1:numsub,topvars]),n_components=2)
# datasetframe.umap = umap(dataframe[1:numsub,varlist],n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
#}
varlistcV <- names(varratio[varratio >= 0.025])
topvars <- univariate_BinEnsemble(DEdataframe[,varlistcV],outcome)
lso <- LASSO_MIN(formula(paste(outcome,"~.")),DEdataframe,family="binomial")
topvars <- unique(c(names(topvars),lso$selectedfeatures))
pander::pander(head(topvars))
FAQ, ADAS13, M_ST29SV, RAVLT_perc_forgetting, RAVLT_learning and MMSE
varlistcV <- varlistcV[varlistcV != outcome]
# DEdataframe[,outcome] <- as.numeric(DEdataframe[,outcome])
#if (nrow(dataframe) < 1000)
#{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,topvars]),n_components=2)
# datasetframe.umap = umap(DEdataframe[1:numsub,varlistcV],n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After ILAA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
#}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
100 : M_ST24SA 200 : D_ST49TA 300 : D_ST47CV 400 : RD_ST24SA
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
100 : La_M_ST24SA 200 : D_ST49TA 300 : D_ST47CV 400 : La_RD_ST24SA
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##top variables
topvar <- c(1:length(varlist)) <= TopVariables
tableRaw <- univarRAW$orderframe[topvar,univariate_columns]
pander::pander(tableRaw)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| ADAS13 | 20.7611 | 6.16923 | 14.0091 | 5.78970 | 0.03549 | 0.788 |
| ADAS11 | 12.8635 | 4.56128 | 8.7155 | 3.84978 | 0.00264 | 0.761 |
| FAQ | 5.4631 | 4.90262 | 1.9266 | 2.98257 | 0.00000 | 0.756 |
| M_ST40CV | 0.1799 | 0.00875 | 0.1875 | 0.00763 | 0.28199 | 0.750 |
| M_ST29SV | 0.1253 | 0.00708 | 0.1321 | 0.00750 | 0.58088 | 0.745 |
| M_ST12SV | 0.0913 | 0.00535 | 0.0962 | 0.00550 | 0.50030 | 0.744 |
| Hippocampus | 0.1582 | 0.00886 | 0.1664 | 0.00945 | 0.44340 | 0.737 |
| RAVLT_immediate | 29.0205 | 7.69236 | 37.2798 | 10.92838 | 0.04406 | 0.728 |
| M_ST24CV | 0.0996 | 0.00800 | 0.1059 | 0.00706 | 0.04673 | 0.727 |
| M_ST31CV | 0.1910 | 0.00945 | 0.1986 | 0.00902 | 0.94566 | 0.717 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
pander::pander(finalTable)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| ADAS13 | 20.761066 | 6.17e+00 | 14.009106 | 5.79e+00 | 3.55e-02 | 0.788 |
| FAQ | 5.463115 | 4.90e+00 | 1.926606 | 2.98e+00 | 0.00e+00 | 0.756 |
| M_ST29SV | 0.125302 | 7.08e-03 | 0.132097 | 7.50e-03 | 5.81e-01 | 0.745 |
| La_RD_ST40CV | 0.000721 | 3.70e-03 | -0.001552 | 2.58e-03 | 2.53e-06 | 0.718 |
| RAVLT_perc_forgetting | 74.353614 | 2.92e+01 | 52.421164 | 3.14e+01 | 1.42e-03 | 0.700 |
| La_RD_ST12SV | 0.000499 | 3.05e-03 | -0.001192 | 3.04e-03 | 1.28e-12 | 0.697 |
| La_D_ST31CV | -0.000036 | 8.45e-04 | 0.000474 | 7.12e-04 | 1.64e-02 | 0.696 |
| La_M_ST24CV | 0.100098 | 5.62e-03 | 0.103429 | 4.96e-03 | 1.78e-01 | 0.691 |
| La_RD_ST26CV | 0.000512 | 2.25e-03 | -0.000811 | 2.08e-03 | 1.64e-07 | 0.690 |
| La_RD_ST58CV | 0.000413 | 2.10e-03 | -0.000648 | 1.46e-03 | 2.45e-06 | 0.687 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 2.64 | 337 | 0.706 |
theCharformulas <- attr(dc,"LatentCharFormulas")
finalTable <- rbind(finalTable,tableRaw[topvar[!(topvar %in% topLAvar)],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- theCharformulas[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| ADAS13 | NA | 20.761066 | 6.17e+00 | 14.009106 | 5.79e+00 | 3.55e-02 | 0.788 | 0.788 | 2 |
| ADAS131 | NA | 20.761066 | 6.17e+00 | 14.009106 | 5.79e+00 | 3.55e-02 | 0.788 | NA | NA |
| ADAS11 | NA | 12.863525 | 4.56e+00 | 8.715528 | 3.85e+00 | 2.64e-03 | 0.761 | 0.761 | NA |
| FAQ | NA | 5.463115 | 4.90e+00 | 1.926606 | 2.98e+00 | 0.00e+00 | 0.756 | 0.756 | 0 |
| FAQ1 | NA | 5.463115 | 4.90e+00 | 1.926606 | 2.98e+00 | 0.00e+00 | 0.756 | NA | NA |
| M_ST40CV | NA | 0.179856 | 8.75e-03 | 0.187527 | 7.63e-03 | 2.82e-01 | 0.750 | 0.750 | NA |
| M_ST29SV | NA | 0.125302 | 7.08e-03 | 0.132097 | 7.50e-03 | 5.81e-01 | 0.745 | 0.745 | 6 |
| M_ST29SV1 | NA | 0.125302 | 7.08e-03 | 0.132097 | 7.50e-03 | 5.81e-01 | 0.745 | NA | NA |
| M_ST12SV | NA | 0.091327 | 5.35e-03 | 0.096219 | 5.50e-03 | 5.00e-01 | 0.744 | 0.744 | NA |
| Hippocampus | NA | 0.158226 | 8.86e-03 | 0.166444 | 9.45e-03 | 4.43e-01 | 0.737 | 0.737 | NA |
| RAVLT_immediate | NA | 29.020492 | 7.69e+00 | 37.279817 | 1.09e+01 | 4.41e-02 | 0.728 | 0.728 | NA |
| M_ST24CV | NA | 0.099630 | 8.00e-03 | 0.105940 | 7.06e-03 | 4.67e-02 | 0.727 | 0.727 | NA |
| La_RD_ST40CV | - (5.536)D_ST40CV + RD_ST40CV | 0.000721 | 3.70e-03 | -0.001552 | 2.58e-03 | 2.53e-06 | 0.718 | 0.585 | 1 |
| M_ST31CV | NA | 0.191026 | 9.45e-03 | 0.198568 | 9.02e-03 | 9.46e-01 | 0.717 | 0.717 | NA |
| RAVLT_perc_forgetting | NA | 74.353614 | 2.92e+01 | 52.421164 | 3.14e+01 | 1.42e-03 | 0.700 | 0.700 | 1 |
| La_RD_ST12SV | - (10.875)D_ST12SV + RD_ST12SV | 0.000499 | 3.05e-03 | -0.001192 | 3.04e-03 | 1.28e-12 | 0.697 | 0.552 | -1 |
| La_D_ST31CV | + D_ST31CV - (0.191)RD_ST31CV | -0.000036 | 8.45e-04 | 0.000474 | 7.12e-04 | 1.64e-02 | 0.696 | 0.511 | 1 |
| La_M_ST24CV | + M_ST24CV - (8.320)D_ST24CV + (0.827)RD_ST24CV | 0.100098 | 5.62e-03 | 0.103429 | 4.96e-03 | 1.78e-01 | 0.691 | 0.727 | -1 |
| La_RD_ST26CV | - (5.692)D_ST26CV + RD_ST26CV | 0.000512 | 2.25e-03 | -0.000811 | 2.08e-03 | 1.64e-07 | 0.690 | 0.534 | 0 |
| La_RD_ST58CV | - (5.448)D_ST58CV + RD_ST58CV | 0.000413 | 2.10e-03 | -0.000648 | 1.46e-03 | 2.45e-06 | 0.687 | 0.554 | 1 |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,scale. = TRUE) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 330 | 106 |
| 1 | 36 | 208 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.791 | 0.759 | 0.821 |
| 3 | se | 0.852 | 0.802 | 0.894 |
| 4 | sp | 0.757 | 0.714 | 0.796 |
| 6 | diag.or | 17.987 | 11.866 | 27.267 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe[,c(outcome,varlistcV)],control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="ILAA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 400 | 36 |
| 1 | 108 | 136 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.788 | 0.756 | 0.818 |
| 3 | se | 0.557 | 0.493 | 0.621 |
| 4 | sp | 0.917 | 0.888 | 0.942 |
| 6 | diag.or | 13.992 | 9.153 | 21.389 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 398 | 38 |
| 1 | 134 | 110 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.747 | 0.713 | 0.779 |
| 3 | se | 0.451 | 0.387 | 0.516 |
| 4 | sp | 0.913 | 0.882 | 0.938 |
| 6 | diag.or | 8.598 | 5.663 | 13.053 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 364 | 72 |
| 1 | 83 | 161 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.772 | 0.739 | 0.803 |
| 3 | se | 0.660 | 0.597 | 0.719 |
| 4 | sp | 0.835 | 0.797 | 0.868 |
| 6 | diag.or | 9.807 | 6.800 | 14.142 |
par(op)